Tuning parallel applications on software-defined supercomputers. Supercomputers are used by many Australian industries and laboratories to make better products and perform critical predictions, and it is essential that codes operate efficiently. This project aims to assist programmers in identifying performance bottlenecks in their code quickly and easily. The project expects to supersede the current methods, which are often complex and time-consuming, by developing innovative software tools and ....Tuning parallel applications on software-defined supercomputers. Supercomputers are used by many Australian industries and laboratories to make better products and perform critical predictions, and it is essential that codes operate efficiently. This project aims to assist programmers in identifying performance bottlenecks in their code quickly and easily. The project expects to supersede the current methods, which are often complex and time-consuming, by developing innovative software tools and techniques. The expected outcomes include novel software, verified by industry partners in real world case studies, ranging from life sciences to hypersonic transport. This should provide significant benefits, including the capacity for Australian industries to access world-class supercomputing technology.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE230100477
Funder
Australian Research Council
Funding Amount
$421,554.00
Summary
Advancing Human Perception: Countering Evolving Malicious Fake Visual Data. The aim of this project is to provide new effective and generalisable deepfake detection methods for automatically detecting maliciously manipulated visual data generated by misused artificial intelligence (AI) techniques. It will present innovative computer vision and image processing knowledge and techniques, enabling the developed methods to advance human perception in recognising fake data, enhance cybersecurity, and ....Advancing Human Perception: Countering Evolving Malicious Fake Visual Data. The aim of this project is to provide new effective and generalisable deepfake detection methods for automatically detecting maliciously manipulated visual data generated by misused artificial intelligence (AI) techniques. It will present innovative computer vision and image processing knowledge and techniques, enabling the developed methods to advance human perception in recognising fake data, enhance cybersecurity, and protect privacy in AI applications. The anticipated outcomes should provide significant benefits to a wide range of applications, such as providing timely alerts to the media, government organisations, and the industry about misleading fake visual data, and preventing financial crimes on synthetic identity fraud.Read moreRead less
Design and verification of correct, efficient and secure concurrent systems. This project aims to provide methods for the design and verification of correct, secure and efficient concurrent software that are scalable and mechanised. Computers with multiple processors are now the norm and are used in a wide range of safety, security and mission critical software applications such as transport, health and infrastructure. These multi-core architectures have the potential to lead to important effici ....Design and verification of correct, efficient and secure concurrent systems. This project aims to provide methods for the design and verification of correct, secure and efficient concurrent software that are scalable and mechanised. Computers with multiple processors are now the norm and are used in a wide range of safety, security and mission critical software applications such as transport, health and infrastructure. These multi-core architectures have the potential to lead to important efficiency gains, but can introduce complex and error-prone behaviours that cannot be managed using traditional software development approaches. This project will produce better, scalable and mechanised methods for the design and verification of such software which is expected to reduce the prevalence of failures in efficient, modern software.Read moreRead less